TMVA::DNN::TDeepNet< Architecture_t, Layer_t > Member List

This is the complete list of members for TMVA::DNN::TDeepNet< Architecture_t, Layer_t >, including all inherited members.

AddBasicRNNLayer(size_t stateSize, size_t inputSize, size_t timeSteps, bool rememberState=false)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
AddBasicRNNLayer(TBasicRNNLayer< Architecture_t > *basicRNNLayer)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
AddConvLayer(size_t depth, size_t filterHeight, size_t filterWidth, size_t strideRows, size_t strideCols, size_t paddingHeight, size_t paddingWidth, EActivationFunction f, Scalar_t dropoutProbability=1.0)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
AddConvLayer(TConvLayer< Architecture_t > *convLayer)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
AddDenseLayer(size_t width, EActivationFunction f, Scalar_t dropoutProbability=1.0)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
AddDenseLayer(TDenseLayer< Architecture_t > *denseLayer)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
AddMaxPoolLayer(size_t frameHeight, size_t frameWidth, size_t strideRows, size_t strideCols, Scalar_t dropoutProbability=1.0)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
AddMaxPoolLayer(CNN::TMaxPoolLayer< Architecture_t > *maxPoolLayer)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
AddReshapeLayer(size_t depth, size_t height, size_t width, bool flattening)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
AddReshapeLayer(TReshapeLayer< Architecture_t > *reshapeLayer)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
Backward(std::vector< Matrix_t > &input, const Matrix_t &groundTruth, const Matrix_t &weights)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
calculateDimension(int imgDim, int fltDim, int padding, int stride)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	private
Clear()	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
fBatchDepth	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	private
fBatchHeight	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	private
fBatchSize	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	private
fBatchWidth	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	private
fI	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	private
fInputDepth	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	private
fInputHeight	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	private
fInputWidth	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	private
fIsTraining	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	private
fJ	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	private
fLayers	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	private
Forward(std::vector< Matrix_t > &input, bool applyDropout=false)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
fR	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	private
fWeightDecay	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	private
GetBatchDepth() const	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
GetBatchHeight() const	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
GetBatchSize() const	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
GetBatchWidth() const	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
GetDepth() const	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
GetInitialization() const	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
GetInputDepth() const	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
GetInputHeight() const	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
GetInputWidth() const	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
GetLayerAt(size_t i)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
GetLayerAt(size_t i) const	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
GetLayers()	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
GetLayers() const	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
GetLossFunction() const	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
GetOutputWidth() const	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
GetRegularization() const	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
GetWeightDecay() const	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
Initialize()	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
isInteger(Scalar_t x) const	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inlineprivate
IsTraining() const	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
Loss(const Matrix_t &groundTruth, const Matrix_t &weights, bool includeRegularization=true) const	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
Loss(std::vector< Matrix_t > &input, const Matrix_t &groundTruth, const Matrix_t &weights, bool applyDropout=false, bool includeRegularization=true)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
Matrix_t typedef	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
ParallelBackward(std::vector< TDeepNet< Architecture_t, Layer_t > > &nets, std::vector< TTensorBatch< Architecture_t > > &batches, Scalar_t learningRate)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
ParallelBackwardMomentum(std::vector< TDeepNet< Architecture_t, Layer_t > > &nets, std::vector< TTensorBatch< Architecture_t > > &batches, Scalar_t learningRate, Scalar_t momentum)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
ParallelBackwardNestorov(std::vector< TDeepNet< Architecture_t, Layer_t > > &nets, std::vector< TTensorBatch< Architecture_t > > &batches, Scalar_t learningRate, Scalar_t momentum)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
ParallelForward(std::vector< TDeepNet< Architecture_t, Layer_t > > &nets, std::vector< TTensorBatch< Architecture_t > > &batches, bool applyDropout=false)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
Prediction(Matrix_t &predictions, EOutputFunction f) const	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
Prediction(Matrix_t &predictions, std::vector< Matrix_t > input, EOutputFunction f)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
Print() const	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
RegularizationTerm() const	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
Scalar_t typedef	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
SetBatchDepth(size_t batchDepth)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
SetBatchHeight(size_t batchHeight)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
SetBatchSize(size_t batchSize)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
SetBatchWidth(size_t batchWidth)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
SetDropoutProbabilities(const std::vector< Double_t > &probabilities)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
SetInitialization(EInitialization I)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
SetInputDepth(size_t inputDepth)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
SetInputHeight(size_t inputHeight)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
SetInputWidth(size_t inputWidth)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
SetLossFunction(ELossFunction J)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
SetRegularization(ERegularization R)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
SetWeightDecay(Scalar_t weightDecay)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >	inline
TDeepNet()	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
TDeepNet(size_t BatchSize, size_t InputDepth, size_t InputHeight, size_t InputWidth, size_t BatchDepth, size_t BatchHeight, size_t BatchWidth, ELossFunction fJ, EInitialization fI=EInitialization::kZero, ERegularization fR=ERegularization::kNone, Scalar_t fWeightDecay=0.0, bool isTraining=false)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
TDeepNet(const TDeepNet &)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
Update(Scalar_t learningRate)	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
~TDeepNet()	TMVA::DNN::TDeepNet< Architecture_t, Layer_t >